Apple Worldwide Developers Conference 1997 - 2010
Andrés Rincón - Santiago Quiroga
2024-12-03
Importacion de textos.
##### importar datos
# Carga de librerías necesarias
suppressMessages(suppressWarnings(library(readr))) # Lectura de archivos CSV
suppressMessages(suppressWarnings(library(tidyverse))) # Conjunto de paquetes para manipulación de datos
# warnings debido a caracteres no UTF-8 o vacios ("")
# UTF-8 (8-bit Unicode Transformation Format) es un formato de codificación de caracteres
# capaz de codificar todos los code points validos en Unicode
# Importar los textos de las conferencias
text_1997 <- read_csv("AppleWWDC1997_es.txt", col_names = FALSE, show_col_types = FALSE)## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)text_2001 <- read_csv("AppleWWDC2001_es.txt", col_names = FALSE, show_col_types = FALSE)## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)text_2005 <- read_csv("AppleWWDC2005_es.txt", col_names = FALSE, show_col_types = FALSE)## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)text_2008 <- read_csv("AppleWWDC2008_es.txt", col_names = FALSE, show_col_types = FALSE)## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)text_2010 <- read_csv("AppleWWDC2010_es.txt", col_names = FALSE, show_col_types = FALSE)## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)class(text_1997)## [1] "spec_tbl_df" "tbl_df" "tbl" "data.frame"text_1997 <- c(text_1997)
class(text_1997)## [1] "list"text_1997 <- unlist(text_1997)
class(text_1997)## [1] "character"names(text_1997) <- NULL # importante!
head(text_1997, n = 3)## [1] "Buenos días"
## [2] "Ambos llevaban corbata toda la semana"
## [3] "Noticias"text_2001 <- unlist(c(read_csv("AppleWWDC2001_es.txt", col_names = FALSE, show_col_types = FALSE)))## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)text_2005 <- unlist(c(read_csv("AppleWWDC2005_es.txt", col_names = FALSE, show_col_types = FALSE)))## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)text_2008 <- unlist(c(read_csv("AppleWWDC2008_es.txt", col_names = FALSE, show_col_types = FALSE)))## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)text_2010 <- unlist(c(read_csv("AppleWWDC2010_es.txt", col_names = FALSE, show_col_types = FALSE)))## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)names(text_2001) <- NULLnames(text_2005) <- NULLnames(text_2008) <- NULLnames(text_2010) <- NULL##### data frame formato tidy
text_1997 <- tibble(line = 1:length(text_1997), text = text_1997) # tibble en lugar de data_frame
class(text_1997)## [1] "tbl_df" "tbl" "data.frame"dim(text_1997)## [1] 1322 2head(text_1997, n = 3)## # A tibble: 3 × 2
## line text
## <int> <chr>
## 1 1 Buenos días
## 2 2 Ambos llevaban corbata toda la semana
## 3 3 Noticias# texto no normalizado
# no tiene "estructura" para analizar
text_2001 <- tibble(line = 1:length(text_2001), text = text_2001)text_2005 <- tibble(line = 1:length(text_2005), text = text_2005)text_2008 <- tibble(line = 1:length(text_2008), text = text_2008)text_2010 <- tibble(line = 1:length(text_2010), text = text_2010)#Tokenizacion
suppressMessages(suppressWarnings(library(tidytext)))
suppressMessages(suppressWarnings(library(magrittr)))
##### tokenizacion formato tidy
text_1997 %<>%
unnest_tokens(input = text, output = word) %>%
filter(!is.na(word)) # importante!
class(text_1997)## [1] "tbl_df" "tbl" "data.frame"dim(text_1997)## [1] 11356 2head(text_1997, n = 10)## # A tibble: 10 × 2
## line word
## <int> <chr>
## 1 1 buenos
## 2 1 días
## 3 2 ambos
## 4 2 llevaban
## 5 2 corbata
## 6 2 toda
## 7 2 la
## 8 2 semana
## 9 3 noticias
## 10 4 tútext_2001 %<>%
unnest_tokens(input = text, output = word) %>%
filter(!is.na(word))
dim(text_2001)## [1] 15099 2text_2005 %<>%
unnest_tokens(input = text, output = word) %>%
filter(!is.na(word))
dim(text_2005)## [1] 8102 2text_2008 %<>%
unnest_tokens(input = text, output = word) %>%
filter(!is.na(word))
dim(text_2008)## [1] 14515 2text_2010 %<>%
unnest_tokens(input = text, output = word) %>%
filter(!is.na(word))
dim(text_2010)## [1] 6412 2head(text_2001, n = 10)## # A tibble: 10 × 2
## line word
## <int> <chr>
## 1 1 buenos
## 2 1 días
## 3 2 estamos
## 4 2 muy
## 5 2 contentos
## 6 2 de
## 7 2 estar
## 8 2 aquí
## 9 2 en
## 10 2 nuevahead(text_2005, n = 10)## # A tibble: 10 × 2
## line word
## <int> <chr>
## 1 1 bienvenidos
## 2 1 a
## 3 1 nuestra
## 4 1 conferencia
## 5 1 mundial
## 6 1 de
## 7 1 desarrolladores
## 8 1 2005
## 9 1 hoy
## 10 1 eshead(text_2008, n = 10)## # A tibble: 10 × 2
## line word
## <int> <chr>
## 1 1 estoy
## 2 1 muy
## 3 1 contento
## 4 1 de
## 5 1 estar
## 6 1 aquí
## 7 1 esta
## 8 1 vez
## 9 2 buenos
## 10 2 díashead(text_2010, n = 10)## # A tibble: 10 × 2
## line word
## <int> <chr>
## 1 1 así
## 2 1 que
## 3 1 volvamos
## 4 1 al
## 5 1 iphone
## 6 2 en
## 7 2 2007
## 8 2 el
## 9 2 iphone
## 10 2 reinventó#Nomrmalizacion de texto
##### texto con numeros?
text_1997 %>%
filter(grepl(pattern = '[0-9]', x = word)) %>%
count(word, sort = TRUE)## # A tibble: 35 × 2
## word n
## <chr> <int>
## 1 10 13
## 2 18 6
## 3 20 4
## 4 100 3
## 5 30 3
## 6 5 3
## 7 500 3
## 8 12 2
## 9 14 2
## 10 15 2
## # ℹ 25 more rowstext_2001 %>%
filter(grepl(pattern = '[0-9]', x = word)) %>%
count(word, sort = TRUE)## # A tibble: 95 × 2
## word n
## <chr> <int>
## 1 10 102
## 2 g4 33
## 3 4 14
## 4 1 13
## 5 3 13
## 6 867 13
## 7 os10 13
## 8 3d 12
## 9 7 12
## 10 99 10
## # ℹ 85 more rowstext_2005 %>%
filter(grepl(pattern = '[0-9]', x = word)) %>%
count(word, sort = TRUE)## # A tibble: 58 × 2
## word n
## <chr> <int>
## 1 10 28
## 2 20 8
## 3 2.1 6
## 4 2 5
## 5 2006 4
## 6 400 4
## 7 264 3
## 8 500 3
## 9 7 3
## 10 9 3
## # ℹ 48 more rowstext_2008 %>%
filter(grepl(pattern = '[0-9]', x = word)) %>%
count(word, sort = TRUE)## # A tibble: 72 × 2
## word n
## <chr> <int>
## 1 3g 33
## 2 2.0 21
## 3 10 20
## 4 70 5
## 5 100 4
## 6 11 4
## 7 199 4
## 8 20 4
## 9 3d 4
## 10 5 4
## # ℹ 62 more rowstext_2010 %>%
filter(grepl(pattern = '[0-9]', x = word)) %>%
count(word, sort = TRUE)## # A tibble: 43 × 2
## word n
## <chr> <int>
## 1 4 42
## 2 3gs 11
## 3 a4 9
## 4 3g 8
## 5 199 5
## 6 2010 5
## 7 2007 4
## 8 24 4
## 9 30 4
## 10 720p 4
## # ℹ 33 more rows##### remover texto con numeros
text_1997 %<>%
filter(!grepl(pattern = '[0-9]', x = word))
dim(text_1997)## [1] 11285 2text_2001 %<>%
filter(!grepl(pattern = '[0-9]', x = word))
dim(text_2001)## [1] 14670 2text_2005 %<>%
filter(!grepl(pattern = '[0-9]', x = word))
dim(text_2005)## [1] 7977 2text_2008 %<>%
filter(!grepl(pattern = '[0-9]', x = word))
dim(text_2008)## [1] 14323 2text_2010 %<>%
filter(!grepl(pattern = '[0-9]', x = word))
dim(text_2010)## [1] 6262 2dim(stop_words)## [1] 1149 2head(stop_words, n = 10)## # A tibble: 10 × 2
## word lexicon
## <chr> <chr>
## 1 a SMART
## 2 a's SMART
## 3 able SMART
## 4 about SMART
## 5 above SMART
## 6 according SMART
## 7 accordingly SMART
## 8 across SMART
## 9 actually SMART
## 10 after SMARTtable(stop_words$lexicon)##
## onix SMART snowball
## 404 571 174###### stop words
# no hay diccionarios en español disponibles en tidytext
# diccionario COUNTWORDSFREE en español (con acentos)
# http://countwordsfree.com/stopwords/spanish
# otras alternativas:
# https://github.com/stopwords-iso/stopwords-es
# de tm::stopwords("spanish")
# se conserva el mismo formato de los diccionarios en tidytext
stop_words_es <- tibble(word = unlist(c(read.table("stop_words_spanish.txt", quote="\"", comment.char=""))), lexicon = "custom")
dim(stop_words_es)## [1] 444 2head(stop_words_es, n = 10)## # A tibble: 10 × 2
## word lexicon
## <chr> <chr>
## 1 algún custom
## 2 alguna custom
## 3 algunas custom
## 4 alguno custom
## 5 algunos custom
## 6 ambos custom
## 7 ampleamos custom
## 8 ante custom
## 9 antes custom
## 10 aquel custom##### remover stop words
text_1997 %<>%
anti_join(x = ., y = stop_words_es)## Joining with `by = join_by(word)`dim(text_1997)## [1] 4009 2head(text_1997, n = 10)## # A tibble: 10 × 2
## line word
## <int> <chr>
## 1 1 días
## 2 2 llevaban
## 3 2 corbata
## 4 2 semana
## 5 3 noticias
## 6 4 tú
## 7 5 corto
## 8 5 haré
## 9 5 uh
## 10 5 sextotext_2001 %<>%
anti_join(x = ., y = stop_words_es)## Joining with `by = join_by(word)`dim(text_2001)## [1] 5957 2head(text_2001, n = 10)## # A tibble: 10 × 2
## line word
## <int> <chr>
## 1 1 días
## 2 2 contentos
## 3 2 york
## 4 2 geniales
## 5 2 compartir
## 6 3 ustedes
## 7 3 mañana
## 8 4 tiendas
## 9 5 tiendas
## 10 5 tyson'stext_2005 %<>%
anti_join(x = ., y = stop_words_es)## Joining with `by = join_by(word)`dim(text_2005)## [1] 3080 2head(text_2005, n = 10)## # A tibble: 10 × 2
## line word
## <int> <chr>
## 1 1 bienvenidos
## 2 1 conferencia
## 3 1 mundial
## 4 1 desarrolladores
## 5 1 día
## 6 1 importante
## 7 2 geniales
## 8 2 ti
## 9 2 quiero
## 10 2 comenzartext_2008 %<>%
anti_join(x = ., y = stop_words_es)## Joining with `by = join_by(word)`dim(text_2008)## [1] 5820 2head(text_2008, n = 10)## # A tibble: 10 × 2
## line word
## <int> <chr>
## 1 1 contento
## 2 2 días
## 3 2 trabajando
## 4 2 duro
## 5 2 geniales
## 6 2 ansiosos
## 7 2 compartir
## 8 2 ustedes
## 9 2 gracias
## 10 2 venirtext_2010 %<>%
anti_join(x = ., y = stop_words_es)## Joining with `by = join_by(word)`dim(text_2010)## [1] 2421 2head(text_2010, n = 10)## # A tibble: 10 × 2
## line word
## <int> <chr>
## 1 1 volvamos
## 2 1 iphone
## 3 2 iphone
## 4 2 reinventó
## 5 2 consideramos
## 6 2 teléfono
## 7 3 difícil
## 8 3 recordar
## 9 3 operadores
## 10 3 iphone##### remover acentos
replacement_list <- list('á' = 'a', 'é' = 'e', 'í' = 'i', 'ó' = 'o', 'ú' = 'u')
text_1997 %<>%
mutate(word = chartr(old = names(replacement_list) %>% str_c(collapse = ''),
new = replacement_list %>% str_c(collapse = ''),
x = word))
dim(text_1997)## [1] 4009 2head(text_1997, n = 10)## # A tibble: 10 × 2
## line word
## <int> <chr>
## 1 1 dias
## 2 2 llevaban
## 3 2 corbata
## 4 2 semana
## 5 3 noticias
## 6 4 tu
## 7 5 corto
## 8 5 hare
## 9 5 uh
## 10 5 sextotext_2001 %<>%
mutate(word = chartr(old = names(replacement_list) %>% str_c(collapse = ''),
new = replacement_list %>% str_c(collapse = ''),
x = word))
dim(text_2001)## [1] 5957 2head(text_2001, n = 10)## # A tibble: 10 × 2
## line word
## <int> <chr>
## 1 1 dias
## 2 2 contentos
## 3 2 york
## 4 2 geniales
## 5 2 compartir
## 6 3 ustedes
## 7 3 mañana
## 8 4 tiendas
## 9 5 tiendas
## 10 5 tyson'stext_2005 %<>%
mutate(word = chartr(old = names(replacement_list) %>% str_c(collapse = ''),
new = replacement_list %>% str_c(collapse = ''),
x = word))
dim(text_2005)## [1] 3080 2head(text_2005, n = 10)## # A tibble: 10 × 2
## line word
## <int> <chr>
## 1 1 bienvenidos
## 2 1 conferencia
## 3 1 mundial
## 4 1 desarrolladores
## 5 1 dia
## 6 1 importante
## 7 2 geniales
## 8 2 ti
## 9 2 quiero
## 10 2 comenzartext_2008 %<>%
mutate(word = chartr(old = names(replacement_list) %>% str_c(collapse = ''),
new = replacement_list %>% str_c(collapse = ''),
x = word))
dim(text_2008)## [1] 5820 2head(text_2008, n = 10)## # A tibble: 10 × 2
## line word
## <int> <chr>
## 1 1 contento
## 2 2 dias
## 3 2 trabajando
## 4 2 duro
## 5 2 geniales
## 6 2 ansiosos
## 7 2 compartir
## 8 2 ustedes
## 9 2 gracias
## 10 2 venirtext_2010 %<>%
mutate(word = chartr(old = names(replacement_list) %>% str_c(collapse = ''),
new = replacement_list %>% str_c(collapse = ''),
x = word))
dim(text_2010)## [1] 2421 2head(text_2010, n = 10)## # A tibble: 10 × 2
## line word
## <int> <chr>
## 1 1 volvamos
## 2 1 iphone
## 3 2 iphone
## 4 2 reinvento
## 5 2 consideramos
## 6 2 telefono
## 7 3 dificil
## 8 3 recordar
## 9 3 operadores
## 10 3 iphone#Tokens mas frecuentes
##### top 10 de tokens mas frecuentes
text_1997 %>%
count(word, sort = TRUE) %>%
head(n = 10)## # A tibble: 10 × 2
## word n
## <chr> <int>
## 1 apple 107
## 2 realmente 59
## 3 um 34
## 4 eh 32
## 5 personas 31
## 6 software 31
## 7 hardware 29
## 8 quiero 28
## 9 gente 26
## 10 mundo 23text_2001 %>%
count(word, sort = TRUE) %>%
head(n = 10)## # A tibble: 10 × 2
## word n
## <chr> <int>
## 1 mac 91
## 2 os 81
## 3 puedes 67
## 4 gracias 46
## 5 rapido 37
## 6 aplicaciones 34
## 7 realmente 30
## 8 apple 28
## 9 sistema 27
## 10 te 24text_2005 %>%
count(word, sort = TRUE) %>%
head(n = 10)## # A tibble: 10 × 2
## word n
## <chr> <int>
## 1 apple 43
## 2 intel 33
## 3 mac 29
## 4 aplicaciones 26
## 5 años 24
## 6 powerpc 24
## 7 xcode 24
## 8 os 23
## 9 transicion 22
## 10 procesadores 18text_2008 %>%
count(word, sort = TRUE) %>%
head(n = 10)## # A tibble: 10 × 2
## word n
## <chr> <int>
## 1 iphone 166
## 2 aplicacion 71
## 3 aplicaciones 55
## 4 realmente 45
## 5 telefono 36
## 6 correo 34
## 7 puedes 34
## 8 sdk 34
## 9 directamente 32
## 10 juego 32text_2010 %>%
count(word, sort = TRUE) %>%
head(n = 10)## # A tibble: 10 × 2
## word n
## <chr> <int>
## 1 iphone 70
## 2 pantalla 29
## 3 telefono 26
## 4 realmente 25
## 5 pixeles 24
## 6 puedes 23
## 7 video 21
## 8 camara 20
## 9 tu 18
## 10 aplicaciones 16##### viz
suppressMessages(suppressWarnings(library(gridExtra)))
p1 <- text_1997 %>%
count(word, sort = TRUE) %>%
slice_max(order_by = n, n = 20) %>% # Mostrar solo las 10 palabras más frecuentes
mutate(word = reorder(word, n)) %>%
ggplot(aes(x = word, y = n)) +
theme_light() +
geom_col(fill = 'darkolivegreen4', alpha = 0.8) +
xlab(NULL) +
ylab("Frecuencia") +
coord_flip() +
ggtitle(label = '1997: Conteo de palabras')
p2 <- text_2001 %>%
count(word, sort = TRUE) %>%
slice_max(order_by = n, n = 20) %>% # Mostrar solo las 10 palabras más frecuentes
mutate(word = reorder(word, n)) %>%
ggplot(aes(x = word, y = n)) +
theme_light() +
geom_col(fill = 'blue4', alpha = 0.8) +
xlab(NULL) +
ylab("Frecuencia") +
coord_flip() +
ggtitle(label = '2001: Conteo de palabras')
# Desplegar gráfico
grid.arrange(p1, p2, ncol = 2)
##### viz
suppressMessages(suppressWarnings(library(gridExtra)))
p1 <- text_1997 %>%
count(word, sort = TRUE) %>%
slice_max(order_by = n, n = 20) %>% # Mostrar solo las 10 palabras más frecuentes
mutate(word = reorder(word, n)) %>%
ggplot(aes(x = word, y = n)) +
theme_light() +
geom_col(fill = 'darkolivegreen4', alpha = 0.8) +
xlab(NULL) +
ylab("Frecuencia") +
coord_flip() +
ggtitle(label = '1997: Conteo de palabras')
p2 <- text_2005 %>%
count(word, sort = TRUE) %>%
slice_max(order_by = n, n = 20) %>% # Mostrar solo las 10 palabras más frecuentes
mutate(word = reorder(word, n)) %>%
ggplot(aes(x = word, y = n)) +
theme_light() +
geom_col(fill = 'blue4', alpha = 0.8) +
xlab(NULL) +
ylab("Frecuencia") +
coord_flip() +
ggtitle(label = '2005: Conteo de palabras')
# Desplegar gráficos
grid.arrange(p1, p2, ncol = 2)
##### viz
suppressMessages(suppressWarnings(library(gridExtra)))
p1 <- text_1997 %>%
count(word, sort = TRUE) %>%
slice_max(order_by = n, n = 20) %>% # Mostrar solo las 20 palabras más frecuentes
mutate(word = reorder(word, n)) %>%
ggplot(aes(x = word, y = n)) +
theme_light() +
geom_col(fill = 'darkolivegreen4', alpha = 0.8) +
xlab(NULL) +
ylab("Frecuencia") +
coord_flip() +
ggtitle(label = '1997: Conteo de palabras')
p2 <- text_2008 %>%
count(word, sort = TRUE) %>%
slice_max(order_by = n, n = 20) %>% # Mostrar solo las 20 palabras más frecuentes
mutate(word = reorder(word, n)) %>%
ggplot(aes(x = word, y = n)) +
theme_light() +
geom_col(fill = 'blue4', alpha = 0.8) +
xlab(NULL) +
ylab("Frecuencia") +
coord_flip() +
ggtitle(label = '2008: Conteo de palabras')
# Desplegar gráficos
grid.arrange(p1, p2, ncol = 2)
##### viz
suppressMessages(suppressWarnings(library(gridExtra)))
p1 <- text_1997 %>%
count(word, sort = TRUE) %>%
slice_max(order_by = n, n = 20) %>% # Mostrar solo las 20 palabras más frecuentes
mutate(word = reorder(word, n)) %>%
ggplot(aes(x = word, y = n)) +
theme_light() +
geom_col(fill = 'darkolivegreen4', alpha = 0.8) +
xlab(NULL) +
ylab("Frecuencia") +
coord_flip() +
ggtitle(label = '1997: Conteo de palabras')
p2 <- text_2010 %>%
count(word, sort = TRUE) %>%
slice_max(order_by = n, n = 20) %>% # Mostrar solo las 20 palabras más frecuentes
mutate(word = reorder(word, n)) %>%
ggplot(aes(x = word, y = n)) +
theme_light() +
geom_col(fill = 'blue4', alpha = 0.8) +
xlab(NULL) +
ylab("Frecuencia") +
coord_flip() +
ggtitle(label = '2010: Conteo de palabras')
# Desplegar gráficos
grid.arrange(p1, p2, ncol = 2)
suppressMessages(suppressWarnings(library(wordcloud)))
###### viz
par(mfrow = c(1,2), mar = c(1,1,2,1), mgp = c(1,1,1))
set.seed(123)
text_1997 %>%
count(word, sort = TRUE) %>%
with(wordcloud(words = word, freq = n, max.words = 20, colors = 'darkolivegreen4'))
title(main = "1997")
set.seed(123)
text_2001 %>%
count(word, sort = TRUE) %>%
with(wordcloud(words = word, freq = n, max.words = 20, colors = 'blue4'))
title(main = "2001")
suppressMessages(suppressWarnings(library(wordcloud)))
###### viz
par(mfrow = c(1,2), mar = c(1,1,2,1), mgp = c(1,1,1))
set.seed(124)
text_1997 %>%
count(word, sort = TRUE) %>%
with(wordcloud(words = word, freq = n, max.words = 20, colors = 'darkolivegreen4'))
title(main = "1997")
set.seed(124)
text_2005 %>%
count(word, sort = TRUE) %>%
with(wordcloud(words = word, freq = n, max.words = 20, colors = 'blue4'))## Warning in wordcloud(words = word, freq = n, max.words = 20, colors = "blue4"):
## apple could not be fit on page. It will not be plotted.## Warning in wordcloud(words = word, freq = n, max.words = 20, colors = "blue4"):
## transicion could not be fit on page. It will not be plotted.## Warning in wordcloud(words = word, freq = n, max.words = 20, colors = "blue4"):
## aplicaciones could not be fit on page. It will not be plotted.title(main = "2005")
suppressMessages(suppressWarnings(library(wordcloud)))
###### viz
par(mfrow = c(1,2), mar = c(1,1,2,1), mgp = c(1,1,1))
set.seed(123)
text_1997 %>%
count(word, sort = TRUE) %>%
with(wordcloud(words = word, freq = n, max.words = 20, colors = 'darkolivegreen4'))
title(main = "1997")
set.seed(123)
text_2008 %>%
count(word, sort = TRUE) %>%
with(wordcloud(words = word, freq = n, max.words = 20, colors = 'blue4'))
title(main = "2008")
suppressMessages(suppressWarnings(library(wordcloud)))
###### viz
par(mfrow = c(1,2), mar = c(1,1,2,1), mgp = c(1,1,1))
set.seed(123)
text_1997 %>%
count(word, sort = TRUE) %>%
with(wordcloud(words = word, freq = n, max.words = 20, colors = 'darkolivegreen4'))
title(main = "1997")
set.seed(123)
text_2010 %>%
count(word, sort = TRUE) %>%
with(wordcloud(words = word, freq = n, max.words = 20, colors = 'blue4'))
title(main = "2010")
##### frecuencias relativas de la palabras
bind_rows(mutate(.data = text_1997, author = "1997"),
mutate(.data = text_2001, author = "2001")) %>%
count(author, word) %>%
group_by(author) %>%
mutate(proportion = n/sum(n)) %>%
select(-n) %>%
spread(author, proportion, fill = 0) -> frec # importante!
frec %<>%
select(word, "1997", "2001")
dim(frec)## [1] 3479 3head(frec, n = 10)## # A tibble: 10 × 3
## word `1997` `2001`
## <chr> <dbl> <dbl>
## 1 abajo 0.000499 0.000336
## 2 abarca 0.000249 0
## 3 abdominales 0.000249 0
## 4 abiertas 0.000249 0.000168
## 5 abierto 0.000998 0.000168
## 6 abiertos 0 0.000168
## 7 abogando 0.000249 0
## 8 aborda 0.000249 0
## 9 abordar 0.000249 0
## 10 abramos 0 0.000168##### top 10 palabras en comun
# orden anidado respecto a petro y duque
frec %>%
filter(1997 !=0, 2001 != 0) %>%
arrange(desc(1997), desc(2001)) -> frec_comun
dim(frec_comun)## [1] 3479 3head(frec_comun, n = 10)## # A tibble: 10 × 3
## word `1997` `2001`
## <chr> <dbl> <dbl>
## 1 abajo 0.000499 0.000336
## 2 abarca 0.000249 0
## 3 abdominales 0.000249 0
## 4 abiertas 0.000249 0.000168
## 5 abierto 0.000998 0.000168
## 6 abiertos 0 0.000168
## 7 abogando 0.000249 0
## 8 aborda 0.000249 0
## 9 abordar 0.000249 0
## 10 abramos 0 0.000168###### proporcion palabras en comun
dim(frec_comun)[1]/dim(frec)[1]## [1] 1##### frecuencias relativas de la palabras
bind_rows(mutate(.data = text_1997, author = "1997"),
mutate(.data = text_2005, author = "2005")) %>%
count(author, word) %>%
group_by(author) %>%
mutate(proportion = n/sum(n)) %>%
select(-n) %>%
spread(author, proportion, fill = 0) -> frec # importante!
frec %<>%
select(word, "1997", "2005")
dim(frec)## [1] 2705 3head(frec, n = 10)## # A tibble: 10 × 3
## word `1997` `2005`
## <chr> <dbl> <dbl>
## 1 abajo 0.000499 0
## 2 abarca 0.000249 0
## 3 abarrotada 0 0.000325
## 4 abdominales 0.000249 0
## 5 abiertas 0.000249 0
## 6 abierto 0.000998 0.000974
## 7 abiertos 0 0.000649
## 8 abogando 0.000249 0
## 9 aborda 0.000249 0
## 10 abordar 0.000249 0##### top 10 palabras en comun
# orden anidado respecto a petro y duque
frec %>%
filter(1997 !=0, 2005 != 0) %>%
arrange(desc(1997), desc(2005)) -> frec_comun1
dim(frec_comun1)## [1] 2705 3head(frec_comun1, n = 10)## # A tibble: 10 × 3
## word `1997` `2005`
## <chr> <dbl> <dbl>
## 1 abajo 0.000499 0
## 2 abarca 0.000249 0
## 3 abarrotada 0 0.000325
## 4 abdominales 0.000249 0
## 5 abiertas 0.000249 0
## 6 abierto 0.000998 0.000974
## 7 abiertos 0 0.000649
## 8 abogando 0.000249 0
## 9 aborda 0.000249 0
## 10 abordar 0.000249 0###### proporcion palabras en comun
dim(frec_comun1)[1]/dim(frec)[1]## [1] 1##### frecuencias relativas de la palabras
bind_rows(mutate(.data = text_1997, author = "1997"),
mutate(.data = text_2008, author = "2008")) %>%
count(author, word) %>%
group_by(author) %>%
mutate(proportion = n/sum(n)) %>%
select(-n) %>%
spread(author, proportion, fill = 0) -> frec2 # importante!
frec2 %<>%
select(word, "1997", "2008")
dim(frec2)## [1] 3395 3head(frec2, n = 10)## # A tibble: 10 × 3
## word `1997` `2008`
## <chr> <dbl> <dbl>
## 1 aaron 0 0.000172
## 2 abajo 0.000499 0.000687
## 3 abandona 0 0.000172
## 4 abandonado 0 0.000172
## 5 abarca 0.000249 0
## 6 abdominales 0.000249 0
## 7 abiertas 0.000249 0
## 8 abierto 0.000998 0.000172
## 9 abiertos 0 0.000172
## 10 abogados 0 0.000172##### top 10 palabras en comun
frec2 %>%
filter(1997 !=0, 2008 != 0) %>%
arrange(desc(1997), desc(2008)) -> frec_comun2
dim(frec_comun2)## [1] 3395 3head(frec_comun2, n = 10)## # A tibble: 10 × 3
## word `1997` `2008`
## <chr> <dbl> <dbl>
## 1 aaron 0 0.000172
## 2 abajo 0.000499 0.000687
## 3 abandona 0 0.000172
## 4 abandonado 0 0.000172
## 5 abarca 0.000249 0
## 6 abdominales 0.000249 0
## 7 abiertas 0.000249 0
## 8 abierto 0.000998 0.000172
## 9 abiertos 0 0.000172
## 10 abogados 0 0.000172###### proporcion palabras en comun
dim(frec_comun2)[1]/dim(frec2)[1]## [1] 1##### frecuencias relativas de la palabras
bind_rows(mutate(.data = text_1997, author = "1997"),
mutate(.data = text_2010, author = "2010")) %>%
count(author, word) %>%
group_by(author) %>%
mutate(proportion = n/sum(n)) %>%
select(-n) %>%
spread(author, proportion, fill = 0) -> frec3 # importante!
frec3 %<>%
select(word, "1997", "2010")
dim(frec3)## [1] 2542 3head(frec3, n = 10)## # A tibble: 10 × 3
## word `1997` `2010`
## <chr> <dbl> <dbl>
## 1 aac 0 0.000413
## 2 abajo 0.000499 0.00165
## 3 abarca 0.000249 0
## 4 abdominales 0.000249 0
## 5 abiertas 0.000249 0.000413
## 6 abierto 0.000998 0.000413
## 7 abiertos 0 0.000413
## 8 abogando 0.000249 0
## 9 aborda 0.000249 0
## 10 abordar 0.000249 0##### top 10 palabras en comun
frec3 %>%
filter(1997 !=0, 2010 != 0) %>%
arrange(desc(1997), desc(2010)) -> frec_comun3
dim(frec_comun3)## [1] 2542 3head(frec_comun3, n = 10)## # A tibble: 10 × 3
## word `1997` `2010`
## <chr> <dbl> <dbl>
## 1 aac 0 0.000413
## 2 abajo 0.000499 0.00165
## 3 abarca 0.000249 0
## 4 abdominales 0.000249 0
## 5 abiertas 0.000249 0.000413
## 6 abierto 0.000998 0.000413
## 7 abiertos 0 0.000413
## 8 abogando 0.000249 0
## 9 aborda 0.000249 0
## 10 abordar 0.000249 0###### proporcion palabras en comun
dim(frec_comun3)[1]/dim(frec3)[1]## [1] 1##### Asignar frecuencias relativas a las variables correspondientes
frec <- bind_rows(
mutate(text_1997, author = "freq_1997"),
mutate(text_2001, author = "freq_2001"),
mutate(text_2005, author = "freq_2005"),
mutate(text_2008, author = "freq_2008"),
mutate(text_2010, author = "freq_2010")
) %>%
# Calcular frecuencias relativas
count(author, word) %>%
group_by(author) %>%
mutate(proportion = n / sum(n)) %>%
select(-n) %>%
spread(author, proportion, fill = 0) # Crear columnas separadas por autor
# Mostrar las dimensiones para verificar
dim(frec)## [1] 5644 6# Verificar las primeras filas
head(frec)## # A tibble: 6 × 6
## word freq_1997 freq_2001 freq_2005 freq_2008 freq_2010
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 aac 0 0 0 0 0.000413
## 2 aaron 0 0 0 0.000172 0
## 3 abajo 0.000499 0.000336 0 0.000687 0.00165
## 4 abandona 0 0 0 0.000172 0
## 5 abandonado 0 0 0 0.000172 0
## 6 abarca 0.000249 0 0 0 0cor.test(x = frec$freq_1997, y = frec$freq_2001)##
## Pearson's product-moment correlation
##
## data: frec$freq_1997 and frec$freq_2001
## t = 29.867, df = 5642, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3467456 0.3918056
## sample estimates:
## cor
## 0.3694928cor.test(x = frec$freq_1997, y = frec$freq_2005)##
## Pearson's product-moment correlation
##
## data: frec$freq_1997 and frec$freq_2005
## t = 41.369, df = 5642, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4621531 0.5021951
## sample estimates:
## cor
## 0.4824261cor.test(x = frec$freq_1997, y = frec$freq_2008)##
## Pearson's product-moment correlation
##
## data: frec$freq_1997 and frec$freq_2008
## t = 20.806, df = 5642, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2425473 0.2910109
## sample estimates:
## cor
## 0.2669479cor.test(x = frec$freq_1997, y = frec$freq_2010)##
## Pearson's product-moment correlation
##
## data: frec$freq_1997 and frec$freq_2010
## t = 16.712, df = 5642, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1921831 0.2419032
## sample estimates:
## cor
## 0.217184# Verifica los nombres de las columnas
names(frec_comun)## [1] "word" "1997" "2001"# Si las columnas son diferentes, renómbralas
colnames(frec_comun) <- c("word", "freq_1997", "freq_2001", "freq_2005", "freq_2008", "freq_2010")
# Filtra las filas con valores no NA
frec_comun <- frec_comun %>%
filter(!is.na(freq_1997) & !is.na(freq_2001))
# Realiza el análisis de correlación
cor.test(x = frec_comun$freq_1997, y = frec_comun$freq_2001)##
## Pearson's product-moment correlation
##
## data: frec_comun$freq_1997 and frec_comun$freq_2001
## t = 20.589, df = 3477, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2997067 0.3589539
## sample estimates:
## cor
## 0.3296548#7 Analisis de sentimiento
# diccionarios
# no hay diccionarios en español disponibles en tidytext
# https://www.kaggle.com/datasets/rtatman/sentiment-lexicons-for-81-languages
positive_words <- read_csv("positive_words_es.txt", col_names = "word", show_col_types = FALSE) %>%
mutate(sentiment = "Positivo")
negative_words <- read_csv("negative_words_es.txt", col_names = "word", show_col_types = FALSE) %>%
mutate(sentiment = "Negativo")
sentiment_words <- bind_rows(positive_words, negative_words)
# comparacion de diccionarios
get_sentiments("bing") %>%
count(sentiment)## # A tibble: 2 × 2
## sentiment n
## <chr> <int>
## 1 negative 4781
## 2 positive 2005sentiment_words %>%
count(sentiment)## # A tibble: 2 × 2
## sentiment n
## <chr> <int>
## 1 Negativo 2720
## 2 Positivo 1555###### viz
suppressMessages(suppressWarnings(library(RColorBrewer)))
# ---------- petro ----------
p1 <- text_1997 %>%
inner_join(sentiment_words) %>%
count(word, sentiment, sort = TRUE) %>%
slice_max(order_by = abs(n), n = 20) %>% # Mostrar las 50 palabras más frecuentes
mutate(n = ifelse(sentiment == "Negativo", -n, n)) %>%
mutate(word = reorder(word, n)) %>%
ggplot(aes(word, n, fill = sentiment)) +
geom_col() +
scale_fill_manual(values = brewer.pal(8, 'Dark2')[c(2, 5)]) +
coord_flip(ylim = c(-7, 7)) + # Ajusta los límites si es necesario
labs(y = "Frecuencia",
x = NULL,
title = "1997: Conteo por sentimiento") +
theme_minimal()## Joining with `by = join_by(word)`##### viz
library(RColorBrewer) # Asegúrate de cargar RColorBrewer si no está cargado
p2 <- text_2001 %>%
inner_join(sentiment_words) %>%
count(word, sentiment, sort = TRUE) %>%
slice_max(order_by = abs(n), n = 20) %>% # Seleccionar las 50 palabras más frecuentes
mutate(n = ifelse(sentiment == "Negativo", -n, n)) %>%
mutate(word = reorder(word, n)) %>%
ggplot(aes(word, n, fill = sentiment)) +
geom_col() +
scale_fill_manual(values = brewer.pal(8,'Dark2')[c(2,5)]) +
coord_flip(ylim = c(-7,7)) + # Ajustar los límites si es necesario
labs(y = "Frecuencia",
x = NULL,
title = "2001: Conteo por sentimiento") +
theme_minimal()## Joining with `by = join_by(word)`grid.arrange(p1, p2, ncol = 2)
text_2005 %>%
inner_join(sentiment_words) %>%
count(word, sentiment, sort = TRUE) %>%
slice_max(order_by = abs(n), n = 20) %>% # Mostrar las 50 palabras más frecuentes
mutate(n = ifelse(sentiment == "Negativo", -n, n)) %>%
mutate(word = reorder(word, n)) %>%
ggplot(aes(word, n, fill = sentiment)) +
geom_col() +
scale_fill_manual(values = brewer.pal(8, 'Dark2')[c(2, 5)]) +
coord_flip(ylim = c(-7, 7)) + # Ajusta los límites si es necesario
labs(y = "Frecuencia",
x = NULL,
title = "2005: Conteo por sentimiento") +
theme_minimal() -> p2## Joining with `by = join_by(word)`grid.arrange(p1, p2, ncol = 2)
text_2008 %>%
inner_join(sentiment_words) %>%
count(word, sentiment, sort = TRUE) %>%
slice_max(order_by = abs(n), n = 20) %>% # Mostrar las 50 palabras más frecuentes
mutate(n = ifelse(sentiment == "Negativo", -n, n)) %>%
mutate(word = reorder(word, n)) %>%
ggplot(aes(word, n, fill = sentiment)) +
geom_col() +
scale_fill_manual(values = brewer.pal(8, 'Dark2')[c(2, 5)]) +
coord_flip(ylim = c(-7, 7)) + # Ajusta los límites si es necesario
labs(y = "Frecuencia",
x = NULL,
title = "2008: Conteo por sentimiento") +
theme_minimal() -> p2## Joining with `by = join_by(word)`grid.arrange(p1, p2, ncol = 2)
text_2010 %>%
inner_join(sentiment_words) %>%
count(word, sentiment, sort = TRUE) %>%
slice_max(order_by = abs(n), n = 20) %>% # Mostrar las 50 palabras más frecuentes
mutate(n = ifelse(sentiment == "Negativo", -n, n)) %>%
mutate(word = reorder(word, n)) %>%
ggplot(aes(word, n, fill = sentiment)) +
geom_col() +
scale_fill_manual(values = brewer.pal(8, 'Dark2')[c(2, 5)]) +
coord_flip(ylim = c(-7, 7)) + # Ajusta los límites si es necesario
labs(y = "Frecuencia",
x = NULL,
title = "2010: Conteo por sentimiento") +
theme_minimal() -> p2## Joining with `by = join_by(word)`grid.arrange(p1, p2, ncol = 2)
suppressMessages(suppressWarnings(library(reshape2))) # acast
##### viz
par(mfrow = c(1,2), mar = c(1,1,2,1), mgp = c(1,1,1))
set.seed(123)
text_1997 %>%
inner_join(sentiment_words) %>%
count(word, sentiment, sort = TRUE) %>%
acast(word ~ sentiment, value.var = "n", fill = 0) %>%
comparison.cloud(colors = brewer.pal(8,'Dark2')[c(2,5)],
max.words = 50, title.size = 1.5)## Joining with `by = join_by(word)`## Warning in comparison.cloud(., colors = brewer.pal(8, "Dark2")[c(2, 5)], :
## problema could not be fit on page. It will not be plotted.## Warning in comparison.cloud(., colors = brewer.pal(8, "Dark2")[c(2, 5)], :
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## errores could not be fit on page. It will not be plotted.## Warning in comparison.cloud(., colors = brewer.pal(8, "Dark2")[c(2, 5)], :
## importante could not be fit on page. It will not be plotted.## Warning in comparison.cloud(., colors = brewer.pal(8, "Dark2")[c(2, 5)], :
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## ventaja could not be fit on page. It will not be plotted.title(main = "1997")
set.seed(123)
text_2001 %>%
inner_join(sentiment_words) %>%
count(word, sentiment, sort = TRUE) %>%
acast(word ~ sentiment, value.var = "n", fill = 0) %>%
comparison.cloud(colors = brewer.pal(8,'Dark2')[c(2,5)],
max.words = 50, title.size = 1.5)## Joining with `by = join_by(word)`## Warning in comparison.cloud(., colors = brewer.pal(8, "Dark2")[c(2, 5)], :
## interrupciones could not be fit on page. It will not be plotted.## Warning in comparison.cloud(., colors = brewer.pal(8, "Dark2")[c(2, 5)], :
## pequeño could not be fit on page. It will not be plotted.title(main = "2001")
suppressMessages(suppressWarnings(library(reshape2))) # acast
##### viz
par(mfrow = c(1,2), mar = c(1,1,2,1), mgp = c(1,1,1))
set.seed(123)
text_1997 %>%
inner_join(sentiment_words) %>%
count(word, sentiment, sort = TRUE) %>%
acast(word ~ sentiment, value.var = "n", fill = 0) %>%
comparison.cloud(colors = brewer.pal(8,'Dark2')[c(2,5)],
max.words = 50, title.size = 1.5)## Joining with `by = join_by(word)`## Warning in comparison.cloud(., colors = brewer.pal(8, "Dark2")[c(2, 5)], :
## problema could not be fit on page. It will not be plotted.## Warning in comparison.cloud(., colors = brewer.pal(8, "Dark2")[c(2, 5)], :
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## triste could not be fit on page. It will not be plotted.## Warning in comparison.cloud(., colors = brewer.pal(8, "Dark2")[c(2, 5)], :
## ventaja could not be fit on page. It will not be plotted.title(main = "1997")
# ---------- ----------
set.seed(123)
text_2005 %>%
inner_join(sentiment_words) %>%
count(word, sentiment, sort = TRUE) %>%
acast(word ~ sentiment, value.var = "n", fill = 0) %>%
comparison.cloud(colors = brewer.pal(8,'Dark2')[c(2,5)],
max.words = 50, title.size = 1.5)## Joining with `by = join_by(word)`## Warning in comparison.cloud(., colors = brewer.pal(8, "Dark2")[c(2, 5)], :
## particularmente could not be fit on page. It will not be plotted.## Warning in comparison.cloud(., colors = brewer.pal(8, "Dark2")[c(2, 5)], :
## separar could not be fit on page. It will not be plotted.title(main = "2005")
suppressMessages(suppressWarnings(library(reshape2))) # acast
##### viz
par(mfrow = c(1,2), mar = c(1,1,2,1), mgp = c(1,1,1))
# ---------- ----------
set.seed(123)
text_1997 %>%
inner_join(sentiment_words) %>%
count(word, sentiment, sort = TRUE) %>%
acast(word ~ sentiment, value.var = "n", fill = 0) %>%
comparison.cloud(colors = brewer.pal(8,'Dark2')[c(2,5)],
max.words = 50, title.size = 1.5)## Joining with `by = join_by(word)`## Warning in comparison.cloud(., colors = brewer.pal(8, "Dark2")[c(2, 5)], :
## problema could not be fit on page. It will not be plotted.## Warning in comparison.cloud(., colors = brewer.pal(8, "Dark2")[c(2, 5)], :
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## importante could not be fit on page. It will not be plotted.## Warning in comparison.cloud(., colors = brewer.pal(8, "Dark2")[c(2, 5)], :
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## ventaja could not be fit on page. It will not be plotted.title(main = "1997")
# ---------- ----------
set.seed(123)
text_2008 %>%
inner_join(sentiment_words) %>%
count(word, sentiment, sort = TRUE) %>%
acast(word ~ sentiment, value.var = "n", fill = 0) %>%
comparison.cloud(colors = brewer.pal(8,'Dark2')[c(2,5)],
max.words = 50, title.size = 1.5)## Joining with `by = join_by(word)`## Warning in comparison.cloud(., colors = brewer.pal(8, "Dark2")[c(2, 5)], : nube
## could not be fit on page. It will not be plotted.## Warning in comparison.cloud(., colors = brewer.pal(8, "Dark2")[c(2, 5)], :
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## interesante could not be fit on page. It will not be plotted.title(main = "2008")
suppressMessages(suppressWarnings(library(reshape2))) # acast
##### viz
par(mfrow = c(1,2), mar = c(1,1,2,1), mgp = c(1,1,1))
# ---------- ----------
set.seed(123)
text_1997 %>%
inner_join(sentiment_words) %>%
count(word, sentiment, sort = TRUE) %>%
acast(word ~ sentiment, value.var = "n", fill = 0) %>%
comparison.cloud(colors = brewer.pal(8,'Dark2')[c(2,5)],
max.words = 50, title.size = 1.5)## Joining with `by = join_by(word)`## Warning in comparison.cloud(., colors = brewer.pal(8, "Dark2")[c(2, 5)], :
## problema could not be fit on page. It will not be plotted.## Warning in comparison.cloud(., colors = brewer.pal(8, "Dark2")[c(2, 5)], :
## usado could not be fit on page. It will not be plotted.## Warning in comparison.cloud(., colors = brewer.pal(8, "Dark2")[c(2, 5)], :
## acuerdo could not be fit on page. It will not be plotted.## Warning in comparison.cloud(., colors = brewer.pal(8, "Dark2")[c(2, 5)], :
## errores could not be fit on page. It will not be plotted.## Warning in comparison.cloud(., colors = brewer.pal(8, "Dark2")[c(2, 5)], :
## importante could not be fit on page. It will not be plotted.## Warning in comparison.cloud(., colors = brewer.pal(8, "Dark2")[c(2, 5)], :
## mejores could not be fit on page. It will not be plotted.## Warning in comparison.cloud(., colors = brewer.pal(8, "Dark2")[c(2, 5)], :
## superior could not be fit on page. It will not be plotted.## Warning in comparison.cloud(., colors = brewer.pal(8, "Dark2")[c(2, 5)], :
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## triste could not be fit on page. It will not be plotted.## Warning in comparison.cloud(., colors = brewer.pal(8, "Dark2")[c(2, 5)], :
## ventaja could not be fit on page. It will not be plotted.title(main = "1997")
# ---------- ----------
set.seed(123)
text_2010 %>%
inner_join(sentiment_words) %>%
count(word, sentiment, sort = TRUE) %>%
acast(word ~ sentiment, value.var = "n", fill = 0) %>%
comparison.cloud(colors = brewer.pal(8,'Dark2')[c(2,5)],
max.words = 50, title.size = 1.5)## Joining with `by = join_by(word)`## Warning in comparison.cloud(., colors = brewer.pal(8, "Dark2")[c(2, 5)], :
## gravedad could not be fit on page. It will not be plotted.## Warning in comparison.cloud(., colors = brewer.pal(8, "Dark2")[c(2, 5)], :
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8
text_1997 <- unlist(c(read_csv("AppleWWDC1997_es.txt", col_names = FALSE, show_col_types = FALSE)))## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)text_2001 <- unlist(c(read_csv("AppleWWDC2001_es.txt", col_names = FALSE, show_col_types = FALSE)))## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)text_2005 <- unlist(c(read_csv("AppleWWDC2005_es.txt", col_names = FALSE, show_col_types = FALSE)))## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)text_2008 <- unlist(c(read_csv("AppleWWDC2008_es.txt", col_names = FALSE, show_col_types = FALSE)))## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)text_2010 <- unlist(c(read_csv("AppleWWDC2010_es.txt", col_names = FALSE, show_col_types = FALSE)))## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)names(text_1997) <- NULL
text_1997 <- tibble(line = 1:length(text_1997), text = text_1997)text_1997 %>%
unnest_tokens(tbl = ., input = text, output = bigram, token = "ngrams", n = 2) %>%
filter(!is.na(bigram)) -> text_1997_bi # importante!
dim(text_1997_bi)## [1] 10362 2names(text_2001) <- NULL
text_2001 <- tibble(line = 1:length(text_2001), text = text_2001)text_2001 %>%
unnest_tokens(tbl = ., input = text, output = bigram, token = "ngrams", n = 2) %>%
filter(!is.na(bigram)) -> text_2001_bi # importante!
dim(text_2001_bi)## [1] 13537 2names(text_2005) <- NULL
text_2005 <- tibble(line = 1:length(text_2005), text = text_2005)text_2005 %>%
unnest_tokens(tbl = ., input = text, output = bigram, token = "ngrams", n = 2) %>%
filter(!is.na(bigram)) -> text_2005_bi # importante!
dim(text_2005_bi)## [1] 7594 2names(text_2008) <- NULL
text_2008 <- tibble(line = 1:length(text_2008), text = text_2008)text_2008 %>%
unnest_tokens(tbl = ., input = text, output = bigram, token = "ngrams", n = 2) %>%
filter(!is.na(bigram)) -> text_2008_bi # importante!
dim(text_2008_bi)## [1] 13615 2names(text_2010) <- NULL
text_2010 <- tibble(line = 1:length(text_2010), text = text_2010)text_2010 %>%
unnest_tokens(tbl = ., input = text, output = bigram, token = "ngrams", n = 2) %>%
filter(!is.na(bigram)) -> text_2010_bi # importante!
dim(text_2010_bi)## [1] 5955 2head(text_1997_bi, n = 10)## # A tibble: 10 × 2
## line bigram
## <int> <chr>
## 1 1 buenos días
## 2 2 ambos llevaban
## 3 2 llevaban corbata
## 4 2 corbata toda
## 5 2 toda la
## 6 2 la semana
## 7 5 corto así
## 8 5 así que
## 9 5 que lo
## 10 5 lo haréhead(text_2005_bi, n = 10)## # A tibble: 10 × 2
## line bigram
## <int> <chr>
## 1 1 bienvenidos a
## 2 1 a nuestra
## 3 1 nuestra conferencia
## 4 1 conferencia mundial
## 5 1 mundial de
## 6 1 de desarrolladores
## 7 1 desarrolladores 2005
## 8 1 2005 hoy
## 9 1 hoy es
## 10 1 es unhead(text_2008_bi, n = 10)## # A tibble: 10 × 2
## line bigram
## <int> <chr>
## 1 1 estoy muy
## 2 1 muy contento
## 3 1 contento de
## 4 1 de estar
## 5 1 estar aquí
## 6 1 aquí esta
## 7 1 esta vez
## 8 2 buenos días
## 9 2 días hemos
## 10 2 hemos estadohead(text_2010_bi, n = 10)## # A tibble: 10 × 2
## line bigram
## <int> <chr>
## 1 1 así que
## 2 1 que volvamos
## 3 1 volvamos al
## 4 1 al iphone
## 5 2 en 2007
## 6 2 2007 el
## 7 2 el iphone
## 8 2 iphone reinventó
## 9 2 reinventó lo
## 10 2 lo que###### top 10 de bigramas mas frecuentes
# hay bigramas que no son interesantes (e.g., "de la")
# esto motiva el uso de stop words nuevamente
text_1997_bi %>%
count(bigram, sort = TRUE) %>%
head(n = 10)## # A tibble: 10 × 2
## bigram n
## <chr> <int>
## 1 creo que 94
## 2 lo que 70
## 3 así que 35
## 4 que apple 34
## 5 y creo 33
## 6 en el 32
## 7 que no 32
## 8 de las 30
## 9 ya sabes 30
## 10 en la 28###### top 10 de bigramas mas frecuentes
# hay bigramas que no son interesantes (e.g., "de la")
# esto motiva el uso de stop words nuevamente
text_2001_bi %>%
count(bigram, sort = TRUE) %>%
head(n = 10)## # A tibble: 10 × 2
## bigram n
## <chr> <int>
## 1 lo que 85
## 2 así que 81
## 3 os 10 69
## 4 en el 66
## 5 de la 53
## 6 mac os 52
## 7 en la 39
## 8 es un 37
## 9 para que 31
## 10 es una 27###### top 10 de bigramas mas frecuentes
# hay bigramas que no son interesantes (e.g., "de la")
# esto motiva el uso de stop words nuevamente
text_2005_bi %>%
count(bigram, sort = TRUE) %>%
head(n = 10)## # A tibble: 10 × 2
## bigram n
## <chr> <int>
## 1 lo que 44
## 2 así que 41
## 3 en el 33
## 4 vamos a 23
## 5 ya sabes 21
## 6 más de 20
## 7 os 10 20
## 8 que es 19
## 9 de apple 18
## 10 de la 18###### top 10 de bigramas mas frecuentes
# hay bigramas que no son interesantes (e.g., "de la")
# esto motiva el uso de stop words nuevamente
text_2008_bi %>%
count(bigram, sort = TRUE) %>%
head(n = 10)## # A tibble: 10 × 2
## bigram n
## <chr> <int>
## 1 en el 89
## 2 así que 76
## 3 el iphone 71
## 4 lo que 69
## 5 en la 61
## 6 de la 43
## 7 para que 39
## 8 con el 29
## 9 voy a 28
## 10 la aplicación 26###### top 10 de bigramas mas frecuentes
# hay bigramas que no son interesantes (e.g., "de la")
# esto motiva el uso de stop words nuevamente
text_2010_bi %>%
count(bigram, sort = TRUE) %>%
head(n = 10)## # A tibble: 10 × 2
## bigram n
## <chr> <int>
## 1 lo que 40
## 2 iphone 4 38
## 3 el iphone 36
## 4 así que 33
## 5 en el 22
## 6 en la 21
## 7 de la 19
## 8 adelante y 17
## 9 para que 17
## 10 es el 15text_1997_bi %>%
separate(bigram, c("word1", "word2"), sep = " ") %>%
filter(!grepl(pattern = '[0-9]', x = word1)) %>%
filter(!grepl(pattern = '[0-9]', x = word2)) %>%
filter(!word1 %in% stop_words_es$word) %>%
filter(!word2 %in% stop_words_es$word) %>%
mutate(word1 = chartr(old = names(replacement_list) %>% str_c(collapse = ''),
new = replacement_list %>% str_c(collapse = ''),
x = word1)) %>%
mutate(word2 = chartr(old = names(replacement_list) %>% str_c(collapse = ''),
new = replacement_list %>% str_c(collapse = ''),
x = word2)) %>%
filter(!is.na(word1)) %>%
filter(!is.na(word2)) %>%
count(word1, word2, sort = TRUE) %>%
rename(weight = n) -> text_1997_bi_counts # importante para la conformacion de la red!
dim(text_1997_bi_counts)## [1] 743 3text_2001_bi %>%
separate(bigram, c("word1", "word2"), sep = " ") %>%
filter(!grepl(pattern = '[0-9]', x = word1)) %>%
filter(!grepl(pattern = '[0-9]', x = word2)) %>%
filter(!word1 %in% stop_words_es$word) %>%
filter(!word2 %in% stop_words_es$word) %>%
mutate(word1 = chartr(old = names(replacement_list) %>% str_c(collapse = ''),
new = replacement_list %>% str_c(collapse = ''),
x = word1)) %>%
mutate(word2 = chartr(old = names(replacement_list) %>% str_c(collapse = ''),
new = replacement_list %>% str_c(collapse = ''),
x = word2)) %>%
filter(!is.na(word1)) %>%
filter(!is.na(word2)) %>%
count(word1, word2, sort = TRUE) %>%
rename(weight = n) -> text_2001_bi_counts # importante para la conformacion de la red!
dim(text_2001_bi_counts)## [1] 1171 3text_2005_bi %>%
separate(bigram, c("word1", "word2"), sep = " ") %>%
filter(!grepl(pattern = '[0-9]', x = word1)) %>%
filter(!grepl(pattern = '[0-9]', x = word2)) %>%
filter(!word1 %in% stop_words_es$word) %>%
filter(!word2 %in% stop_words_es$word) %>%
mutate(word1 = chartr(old = names(replacement_list) %>% str_c(collapse = ''),
new = replacement_list %>% str_c(collapse = ''),
x = word1)) %>%
mutate(word2 = chartr(old = names(replacement_list) %>% str_c(collapse = ''),
new = replacement_list %>% str_c(collapse = ''),
x = word2)) %>%
filter(!is.na(word1)) %>%
filter(!is.na(word2)) %>%
count(word1, word2, sort = TRUE) %>%
rename(weight = n) -> text_2005_bi_counts # importante para la conformacion de la red!
dim(text_2005_bi_counts)## [1] 588 3text_2008_bi %>%
separate(bigram, c("word1", "word2"), sep = " ") %>%
filter(!grepl(pattern = '[0-9]', x = word1)) %>%
filter(!grepl(pattern = '[0-9]', x = word2)) %>%
filter(!word1 %in% stop_words_es$word) %>%
filter(!word2 %in% stop_words_es$word) %>%
mutate(word1 = chartr(old = names(replacement_list) %>% str_c(collapse = ''),
new = replacement_list %>% str_c(collapse = ''),
x = word1)) %>%
mutate(word2 = chartr(old = names(replacement_list) %>% str_c(collapse = ''),
new = replacement_list %>% str_c(collapse = ''),
x = word2)) %>%
filter(!is.na(word1)) %>%
filter(!is.na(word2)) %>%
count(word1, word2, sort = TRUE) %>%
rename(weight = n) -> text_2008_bi_counts # importante para la conformacion de la red!
dim(text_2008_bi_counts)## [1] 1188 3text_2010_bi %>%
separate(bigram, c("word1", "word2"), sep = " ") %>%
filter(!grepl(pattern = '[0-9]', x = word1)) %>%
filter(!grepl(pattern = '[0-9]', x = word2)) %>%
filter(!word1 %in% stop_words_es$word) %>%
filter(!word2 %in% stop_words_es$word) %>%
mutate(word1 = chartr(old = names(replacement_list) %>% str_c(collapse = ''),
new = replacement_list %>% str_c(collapse = ''),
x = word1)) %>%
mutate(word2 = chartr(old = names(replacement_list) %>% str_c(collapse = ''),
new = replacement_list %>% str_c(collapse = ''),
x = word2)) %>%
filter(!is.na(word1)) %>%
filter(!is.na(word2)) %>%
count(word1, word2, sort = TRUE) %>%
rename(weight = n) -> text_2010_bi_counts # importante para la conformacion de la red!
dim(text_2010_bi_counts)## [1] 508 3head(text_1997_bi_counts, n = 10)## # A tibble: 10 × 3
## word1 word2 weight
## <chr> <chr> <int>
## 1 rap city 8
## 2 correo electronico 7
## 3 street journal 6
## 4 wall street 6
## 5 productos realmente 5
## 6 apple deberia 4
## 7 disco duro 4
## 8 pequeña cosa 4
## 9 and play 3
## 10 apple necesita 3head(text_2001_bi_counts, n = 10)## # A tibble: 10 × 3
## word1 word2 weight
## <chr> <chr> <int>
## 1 mac os 52
## 2 sistema operativo 9
## 3 super drive 9
## 4 power mac 8
## 5 gracias steve 7
## 6 disco duro 6
## 7 power max 6
## 8 centro comercial 5
## 9 grabar dvd 5
## 10 libro mundial 5head(text_2005_bi_counts, n = 10)## # A tibble: 10 × 3
## word1 word2 weight
## <chr> <chr> <int>
## 1 sistema operativo 12
## 2 mac os 10
## 3 binarios universales 7
## 4 has visto 7
## 5 codigo fuente 6
## 6 procesadores intel 6
## 7 dejame abrir 5
## 8 excelentes productos 5
## 9 dejame mostrarte 4
## 10 wolfram research 4head(text_2008_bi_counts, n = 10)## # A tibble: 10 × 3
## word1 word2 weight
## <chr> <chr> <int>
## 1 correo electronico 25
## 2 gustaria invitar 12
## 3 app store 11
## 4 mis contactos 8
## 5 sitio web 6
## 6 correos electronicos 5
## 7 dispositivo movil 5
## 8 interfaz web 5
## 9 mac os 5
## 10 tu iphone 5head(text_2010_bi_counts, n = 10)## # A tibble: 10 × 3
## word1 word2 weight
## <chr> <chr> <int>
## 1 pantalla retina 14
## 2 acero inoxidable 7
## 3 wi fi 7
## 4 estaciones base 5
## 5 tu telefono 5
## 6 alta definicion 4
## 7 flash led 4
## 8 computadoras portatiles 3
## 9 has visto 3
## 10 iphone original 3##### definir una red a partir de la frecuencia (weight) de los bigramas
# binaria, no dirigida, ponderada, simple
# se recomienda variar el umbral del filtro y construir bigramas no consecutivos para obtener redes con mayor informacion
suppressMessages(suppressWarnings(library(igraph)))
# Crear la red
g <- text_1997_bi_counts %>%
filter(weight > 2) %>%
graph_from_data_frame(directed = FALSE)
# Ajuste visual
set.seed(123)
plot(g,
layout = layout_with_kk, # Layout con más dispersión
vertex.color = 1,
vertex.frame.color = 1,
vertex.size = 6, # Tamaño de los vértices mayor para mayor separación
vertex.label.color = 'black',
vertex.label.cex = 0.6, # Reducir un poco el tamaño de la fuente
vertex.label.dist = 3, # Aumenté la distancia entre las etiquetas y los vértices
main = "1997 - Umbral = 2")
##### definir una red a partir de la frecuencia (weight) de los bigramas
# binaria, no dirigida, ponderada, simple
# se recomienda variar el umbral del filtro y construir bigramas no consecutivos para obtener redes con mayor informacion
suppressMessages(suppressWarnings(library(igraph)))
# Crear la red
g <- text_2001_bi_counts %>%
filter(weight > 3) %>%
graph_from_data_frame(directed = FALSE)
# Ajuste visual
set.seed(123)
plot(g,
layout = layout_with_kk, # Layout con más dispersión
vertex.color = 1,
vertex.frame.color = 1,
vertex.size = 6, # Tamaño de los vértices mayor para mayor separación
vertex.label.color = 'black',
vertex.label.cex = 0.6, # Reducir un poco el tamaño de la fuente
vertex.label.dist = 3, # Aumenté la distancia entre las etiquetas y los vértices
main = "2001 - Umbral = 3")
##### definir una red a partir de la frecuencia (weight) de los bigramas
# binaria, no dirigida, ponderada, simple
# se recomienda variar el umbral del filtro y construir bigramas no consecutivos para obtener redes con mayor informacion
suppressMessages(suppressWarnings(library(igraph)))
# Crear la red
g <- text_2005_bi_counts %>%
filter(weight > 4) %>%
graph_from_data_frame(directed = FALSE)
# Ajuste visual
set.seed(123)
plot(g,
layout = layout_with_kk, # Layout con más dispersión
vertex.color = 1,
vertex.frame.color = 1,
vertex.size = 6, # Tamaño de los vértices mayor para mayor separación
vertex.label.color = 'black',
vertex.label.cex = 0.6, # Reducir un poco el tamaño de la fuente
vertex.label.dist = 3, # Aumenté la distancia entre las etiquetas y los vértices
main = "2005 - Umbral = 4")
##### definir una red a partir de la frecuencia (weight) de los bigramas
# binaria, no dirigida, ponderada, simple
# se recomienda variar el umbral del filtro y construir bigramas no consecutivos para obtener redes con mayor informacion
suppressMessages(suppressWarnings(library(igraph)))
# Crear la red
g <- text_2008_bi_counts %>%
filter(weight > 3) %>%
graph_from_data_frame(directed = FALSE)
# Ajuste visual
set.seed(123)
plot(g,
layout = layout_with_kk, # Layout con más dispersión
vertex.color = 1,
vertex.frame.color = 1,
vertex.size = 6, # Tamaño de los vértices mayor para mayor separación
vertex.label.color = 'black',
vertex.label.cex = 0.6, # Reducir un poco el tamaño de la fuente
vertex.label.dist = 3, # Aumenté la distancia entre las etiquetas y los vértices
main = "2008 - Umbral = 3")
##### definir una red a partir de la frecuencia (weight) de los bigramas
# binaria, no dirigida, ponderada, simple
# se recomienda variar el umbral del filtro y construir bigramas no consecutivos para obtener redes con mayor informacion
suppressMessages(suppressWarnings(library(igraph)))
# Crear la red
g <- text_2010_bi_counts %>%
filter(weight > 2) %>%
graph_from_data_frame(directed = FALSE)
# Ajuste visual
set.seed(123)
plot(g,
layout = layout_with_kk, # Layout con más dispersión
vertex.color = 1,
vertex.frame.color = 1,
vertex.size = 6, # Tamaño de los vértices mayor para mayor separación
vertex.label.color = 'black',
vertex.label.cex = 0.6, # Reducir un poco el tamaño de la fuente
vertex.label.dist = 3, # Aumenté la distancia entre las etiquetas y los vértices
main = "2010 - Umbral = 2")
##### red con un umbral diferente
g <- text_1997_bi_counts %>%
filter(weight > 1) %>%
graph_from_data_frame(directed = FALSE)
# viz
set.seed(123)
plot(g, layout = layout_with_kk, vertex.color = 1, vertex.frame.color = 1, vertex.size = 3, vertex.label = NA, main = "1997 - Umbral = 1")
##### red con un umbral diferente
g <- text_2001_bi_counts %>%
filter(weight > 1) %>%
graph_from_data_frame(directed = FALSE)
# viz
set.seed(123)
plot(g, layout = layout_with_kk, vertex.color = 1, vertex.frame.color = 1, vertex.size = 3, vertex.label = NA, main = "2001 - Umbral = 1")
##### red con un umbral diferente
g <- text_2005_bi_counts %>%
filter(weight > 1) %>%
graph_from_data_frame(directed = FALSE)
# viz
set.seed(123)
plot(g, layout = layout_with_kk, vertex.color = 1, vertex.frame.color = 1, vertex.size = 3, vertex.label = NA, main = "2005 - Umbral = 1")
##### red con un umbral diferente
g <- text_2008_bi_counts %>%
filter(weight > 1) %>%
graph_from_data_frame(directed = FALSE)
# viz
set.seed(123)
plot(g, layout = layout_with_kk, vertex.color = 1, vertex.frame.color = 1, vertex.size = 3, vertex.label = NA, main = "2008 - Umbral = 1")
##### red con un umbral diferente
g <- text_2010_bi_counts %>%
filter(weight > 1) %>%
graph_from_data_frame(directed = FALSE)
# viz
set.seed(123)
plot(g, layout = layout_with_kk, vertex.color = 1, vertex.frame.color = 1, vertex.size = 3, vertex.label = NA, main = "2010 - Umbral = 1")
##### componente conexa mas grande de la red
g <- text_1997_bi_counts %>%
filter(weight > 1) %>%
graph_from_data_frame(directed = FALSE)
# grafo inducido por la componente conexa
V(g)$cluster <- clusters(graph = g)$membership## Warning: `clusters()` was deprecated in igraph 2.0.0.
## ℹ Please use `components()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.gcc <- induced_subgraph(graph = g, vids = which(V(g)$cluster == which.max(clusters(graph = g)$csize)))
par(mfrow = c(1,2), mar = c(1,1,2,1), mgp = c(1,1,1))
set.seed(123)
plot(gcc, layout = layout_with_kk, vertex.color = adjustcolor('darkolivegreen4', 0.1), vertex.frame.color = 'darkolivegreen4', vertex.size = 2*strength(gcc), vertex.label.color = 'black', vertex.label.cex = 0.9, vertex.label.dist = 1, edge.width = 3*E(g)$weight/max(E(g)$weight))
title(main = "Componente conexa", outer = T, line = -1)
##### componente conexa mas grande de la red
g <- text_2001_bi_counts %>%
filter(weight > 1) %>%
graph_from_data_frame(directed = FALSE)
# grafo inducido por la componente conexa
V(g)$cluster <- clusters(graph = g)$membershipgcc <- induced_subgraph(graph = g, vids = which(V(g)$cluster == which.max(clusters(graph = g)$csize)))
par(mfrow = c(1,2), mar = c(1,1,2,1), mgp = c(1,1,1))
# viz 2
set.seed(123)
plot(gcc, layout = layout_with_kk, vertex.color = adjustcolor('darkolivegreen4', 0.1), vertex.frame.color = 'darkolivegreen4', vertex.size = 2*strength(gcc), vertex.label.color = 'black', vertex.label.cex = 0.9, vertex.label.dist = 1, edge.width = 3*E(g)$weight/max(E(g)$weight))
title(main = "Componente conexa", outer = T, line = -1)
##### componente conexa mas grande de la red
g <- text_2005_bi_counts %>%
filter(weight > 1) %>%
graph_from_data_frame(directed = FALSE)
# grafo inducido por la componente conexa
V(g)$cluster <- clusters(graph = g)$membershipgcc <- induced_subgraph(graph = g, vids = which(V(g)$cluster == which.max(clusters(graph = g)$csize)))
par(mfrow = c(1,2), mar = c(1,1,2,1), mgp = c(1,1,1))
# viz 2
set.seed(123)
plot(gcc, layout = layout_with_kk, vertex.color = adjustcolor('darkolivegreen4', 0.1), vertex.frame.color = 'darkolivegreen4', vertex.size = 2*strength(gcc), vertex.label.color = 'black', vertex.label.cex = 0.9, vertex.label.dist = 1, edge.width = 3*E(g)$weight/max(E(g)$weight))
title(main = "Componente conexa", outer = T, line = -1)
##### componente conexa mas grande de la red
g <- text_2008_bi_counts %>%
filter(weight > 2) %>%
graph_from_data_frame(directed = FALSE)
# grafo inducido por la componente conexa
V(g)$cluster <- clusters(graph = g)$membershipgcc <- induced_subgraph(graph = g, vids = which(V(g)$cluster == which.max(clusters(graph = g)$csize)))
par(mfrow = c(1,2), mar = c(1,1,2,1), mgp = c(1,1,1))
# viz 2
set.seed(123)
plot(gcc, layout = layout_with_kk, vertex.color = adjustcolor('darkolivegreen4', 0.1), vertex.frame.color = 'darkolivegreen4', vertex.size = 2*strength(gcc), vertex.label.color = 'black', vertex.label.cex = 0.9, vertex.label.dist = 1, edge.width = 3*E(g)$weight/max(E(g)$weight))
title(main = "Componente conexa", outer = T, line = -1)
##### componente conexa mas grande de la red
g <- text_2010_bi_counts %>%
filter(weight > 1) %>%
graph_from_data_frame(directed = FALSE)
# grafo inducido por la componente conexa
V(g)$cluster <- clusters(graph = g)$membershipgcc <- induced_subgraph(graph = g, vids = which(V(g)$cluster == which.max(clusters(graph = g)$csize)))
par(mfrow = c(1,2), mar = c(1,1,2,1), mgp = c(1,1,1))
# viz 2
set.seed(123)
plot(gcc, layout = layout_with_kk, vertex.color = adjustcolor('darkolivegreen4', 0.1), vertex.frame.color = 'darkolivegreen4', vertex.size = 2*strength(gcc), vertex.label.color = 'black', vertex.label.cex = 0.9, vertex.label.dist = 1, edge.width = 3*E(g)$weight/max(E(g)$weight))
title(main = "Componente conexa", outer = T, line = -1)
9
##### importar datos
text_1997 <- unlist(c(read_csv("AppleWWDC1997_es.txt", col_names = FALSE, show_col_types = FALSE)))## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)text_2001 <- unlist(c(read_csv("AppleWWDC2001_es.txt", col_names = FALSE, show_col_types = FALSE)))## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)text_2005 <- unlist(c(read_csv("AppleWWDC2005_es.txt", col_names = FALSE, show_col_types = FALSE)))## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)text_2008 <- unlist(c(read_csv("AppleWWDC2008_es.txt", col_names = FALSE, show_col_types = FALSE)))## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)text_2010 <- unlist(c(read_csv("AppleWWDC2010_es.txt", col_names = FALSE, show_col_types = FALSE)))## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)names(text_1997) <- NULL
text_1997 <- tibble(line = 1:length(text_1997), text = text_1997)names(text_2001) <- NULL
text_2001 <- tibble(line = 1:length(text_2001), text = text_2001)names(text_2005) <- NULL
text_2005 <- tibble(line = 1:length(text_2005), text = text_2005)names(text_2008) <- NULL
text_2008 <- tibble(line = 1:length(text_2008), text = text_2008)names(text_2010) <- NULL
text_2010 <- tibble(line = 1:length(text_2010), text = text_2010)##### tokenizar en skip-gram
# en este caso cada token es un unigrama o un bigrama regular o un bigrama con espaciamiento
text_1997 %>%
unnest_tokens(tbl = ., input = text, output = skipgram, token = "skip_ngrams", n = 2) %>%
filter(!is.na(skipgram)) -> text_1997_skip
dim(text_1997_skip)## [1] 31144 2text_2001 %>%
unnest_tokens(tbl = ., input = text, output = skipgram, token = "skip_ngrams", n = 2) %>%
filter(!is.na(skipgram)) -> text_2001_skip
dim(text_2001_skip)## [1] 40728 2text_2005 %>%
unnest_tokens(tbl = ., input = text, output = skipgram, token = "skip_ngrams", n = 2) %>%
filter(!is.na(skipgram)) -> text_2005_skip
dim(text_2005_skip)## [1] 22792 2text_2008 %>%
unnest_tokens(tbl = ., input = text, output = skipgram, token = "skip_ngrams", n = 2) %>%
filter(!is.na(skipgram)) -> text_2008_skip
dim(text_2008_skip)## [1] 40861 2text_2010 %>%
unnest_tokens(tbl = ., input = text, output = skipgram, token = "skip_ngrams", n = 2) %>%
filter(!is.na(skipgram)) -> text_2010_skip
dim(text_2010_skip)## [1] 17871 2head(text_1997_skip, n = 10)## # A tibble: 10 × 2
## line skipgram
## <int> <chr>
## 1 1 buenos
## 2 1 buenos días
## 3 1 días
## 4 2 ambos
## 5 2 ambos llevaban
## 6 2 ambos corbata
## 7 2 llevaban
## 8 2 llevaban corbata
## 9 2 llevaban toda
## 10 2 corbatahead(text_2001_skip, n = 10)## # A tibble: 10 × 2
## line skipgram
## <int> <chr>
## 1 1 buenos
## 2 1 buenos días
## 3 1 días
## 4 2 estamos
## 5 2 estamos muy
## 6 2 estamos contentos
## 7 2 muy
## 8 2 muy contentos
## 9 2 muy de
## 10 2 contentoshead(text_2005_skip, n = 10)## # A tibble: 10 × 2
## line skipgram
## <int> <chr>
## 1 1 bienvenidos
## 2 1 bienvenidos a
## 3 1 bienvenidos nuestra
## 4 1 a
## 5 1 a nuestra
## 6 1 a conferencia
## 7 1 nuestra
## 8 1 nuestra conferencia
## 9 1 nuestra mundial
## 10 1 conferenciahead(text_2008_skip, n = 10)## # A tibble: 10 × 2
## line skipgram
## <int> <chr>
## 1 1 estoy
## 2 1 estoy muy
## 3 1 estoy contento
## 4 1 muy
## 5 1 muy contento
## 6 1 muy de
## 7 1 contento
## 8 1 contento de
## 9 1 contento estar
## 10 1 dehead(text_2010_skip, n = 10)## # A tibble: 10 × 2
## line skipgram
## <int> <chr>
## 1 1 así
## 2 1 así que
## 3 1 así volvamos
## 4 1 que
## 5 1 que volvamos
## 6 1 que al
## 7 1 volvamos
## 8 1 volvamos al
## 9 1 volvamos iphone
## 10 1 alsuppressMessages(suppressWarnings(library(ngram)))
# contar palabras en cada skip-gram
text_1997_skip$num_words <- text_1997_skip$skipgram %>%
map_int(.f = ~ wordcount(.x))
head(text_1997_skip, n = 10)## # A tibble: 10 × 3
## line skipgram num_words
## <int> <chr> <int>
## 1 1 buenos 1
## 2 1 buenos días 2
## 3 1 días 1
## 4 2 ambos 1
## 5 2 ambos llevaban 2
## 6 2 ambos corbata 2
## 7 2 llevaban 1
## 8 2 llevaban corbata 2
## 9 2 llevaban toda 2
## 10 2 corbata 1# remover unigramas
text_1997_skip %<>%
filter(num_words == 2) %>%
select(-num_words)
dim(text_1997_skip)## [1] 19788 2suppressMessages(suppressWarnings(library(ngram)))
# contar palabras en cada skip-gram
text_2001_skip$num_words1 <- text_2001_skip$skipgram %>%
map_int(.f = ~ wordcount(.x))
head(text_2001_skip, n = 10)## # A tibble: 10 × 3
## line skipgram num_words1
## <int> <chr> <int>
## 1 1 buenos 1
## 2 1 buenos días 2
## 3 1 días 1
## 4 2 estamos 1
## 5 2 estamos muy 2
## 6 2 estamos contentos 2
## 7 2 muy 1
## 8 2 muy contentos 2
## 9 2 muy de 2
## 10 2 contentos 1text_2001_skip %<>%
filter(num_words1 == 2) %>%
select(-num_words1)
dim(text_2001_skip)## [1] 25629 2suppressMessages(suppressWarnings(library(ngram)))
# contar palabras en cada skip-gram
text_2005_skip$num_words2 <- text_2005_skip$skipgram %>%
map_int(.f = ~ wordcount(.x))
head(text_2005_skip, n = 10)## # A tibble: 10 × 3
## line skipgram num_words2
## <int> <chr> <int>
## 1 1 bienvenidos 1
## 2 1 bienvenidos a 2
## 3 1 bienvenidos nuestra 2
## 4 1 a 1
## 5 1 a nuestra 2
## 6 1 a conferencia 2
## 7 1 nuestra 1
## 8 1 nuestra conferencia 2
## 9 1 nuestra mundial 2
## 10 1 conferencia 1text_2005_skip %<>%
filter(num_words2 == 2) %>%
select(-num_words2)
dim(text_2005_skip)## [1] 14690 2suppressMessages(suppressWarnings(library(ngram)))
# contar palabras en cada skip-gram
text_2008_skip$num_words3 <- text_2008_skip$skipgram %>%
map_int(.f = ~ wordcount(.x))
head(text_2008_skip, n = 10)## # A tibble: 10 × 3
## line skipgram num_words3
## <int> <chr> <int>
## 1 1 estoy 1
## 2 1 estoy muy 2
## 3 1 estoy contento 2
## 4 1 muy 1
## 5 1 muy contento 2
## 6 1 muy de 2
## 7 1 contento 1
## 8 1 contento de 2
## 9 1 contento estar 2
## 10 1 de 1text_2008_skip %<>%
filter(num_words3 == 2) %>%
select(-num_words3)
dim(text_2008_skip)## [1] 26346 2suppressMessages(suppressWarnings(library(ngram)))
# contar palabras en cada skip-gram
text_2010_skip$num_words4 <- text_2010_skip$skipgram %>%
map_int(.f = ~ wordcount(.x))
head(text_2010_skip, n = 10)## # A tibble: 10 × 3
## line skipgram num_words4
## <int> <chr> <int>
## 1 1 así 1
## 2 1 así que 2
## 3 1 así volvamos 2
## 4 1 que 1
## 5 1 que volvamos 2
## 6 1 que al 2
## 7 1 volvamos 1
## 8 1 volvamos al 2
## 9 1 volvamos iphone 2
## 10 1 al 1text_2010_skip %<>%
filter(num_words4 == 2) %>%
select(-num_words4)
dim(text_2010_skip)## [1] 11459 2head(text_1997_skip, n = 10)## # A tibble: 10 × 2
## line skipgram
## <int> <chr>
## 1 1 buenos días
## 2 2 ambos llevaban
## 3 2 ambos corbata
## 4 2 llevaban corbata
## 5 2 llevaban toda
## 6 2 corbata toda
## 7 2 corbata la
## 8 2 toda la
## 9 2 toda semana
## 10 2 la semanahead(text_2001_skip, n = 10)## # A tibble: 10 × 2
## line skipgram
## <int> <chr>
## 1 1 buenos días
## 2 2 estamos muy
## 3 2 estamos contentos
## 4 2 muy contentos
## 5 2 muy de
## 6 2 contentos de
## 7 2 contentos estar
## 8 2 de estar
## 9 2 de aquí
## 10 2 estar aquíhead(text_2005_skip, n = 10)## # A tibble: 10 × 2
## line skipgram
## <int> <chr>
## 1 1 bienvenidos a
## 2 1 bienvenidos nuestra
## 3 1 a nuestra
## 4 1 a conferencia
## 5 1 nuestra conferencia
## 6 1 nuestra mundial
## 7 1 conferencia mundial
## 8 1 conferencia de
## 9 1 mundial de
## 10 1 mundial desarrolladoreshead(text_2008_skip, n = 10)## # A tibble: 10 × 2
## line skipgram
## <int> <chr>
## 1 1 estoy muy
## 2 1 estoy contento
## 3 1 muy contento
## 4 1 muy de
## 5 1 contento de
## 6 1 contento estar
## 7 1 de estar
## 8 1 de aquí
## 9 1 estar aquí
## 10 1 estar estahead(text_2010_skip, n = 10)## # A tibble: 10 × 2
## line skipgram
## <int> <chr>
## 1 1 así que
## 2 1 así volvamos
## 3 1 que volvamos
## 4 1 que al
## 5 1 volvamos al
## 6 1 volvamos iphone
## 7 1 al iphone
## 8 2 en 2007
## 9 2 en el
## 10 2 2007 el##### omitir stop words
text_1997_skip %>%
separate(skipgram, c("word1", "word2"), sep = " ") %>%
filter(!grepl(pattern = '[0-9]', x = word1)) %>%
filter(!grepl(pattern = '[0-9]', x = word2)) %>%
filter(!word1 %in% stop_words_es$word) %>%
filter(!word2 %in% stop_words_es$word) %>%
mutate(word1 = chartr(old = names(replacement_list) %>% str_c(collapse = ''),
new = replacement_list %>% str_c(collapse = ''),
x = word1)) %>%
mutate(word2 = chartr(old = names(replacement_list) %>% str_c(collapse = ''),
new = replacement_list %>% str_c(collapse = ''),
x = word2)) %>%
filter(!is.na(word1)) %>%
filter(!is.na(word2)) %>%
count(word1, word2, sort = TRUE) %>%
rename(weight = n) -> text_1997_skip_counts
dim(text_1997_skip_counts)## [1] 1852 3text_2001_skip %>%
separate(skipgram, c("word1", "word2"), sep = " ") %>%
filter(!grepl(pattern = '[0-9]', x = word1)) %>%
filter(!grepl(pattern = '[0-9]', x = word2)) %>%
filter(!word1 %in% stop_words_es$word) %>%
filter(!word2 %in% stop_words_es$word) %>%
mutate(word1 = chartr(old = names(replacement_list) %>% str_c(collapse = ''),
new = replacement_list %>% str_c(collapse = ''),
x = word1)) %>%
mutate(word2 = chartr(old = names(replacement_list) %>% str_c(collapse = ''),
new = replacement_list %>% str_c(collapse = ''),
x = word2)) %>%
filter(!is.na(word1)) %>%
filter(!is.na(word2)) %>%
count(word1, word2, sort = TRUE) %>%
rename(weight = n) -> text_2001_skip_counts
dim(text_2001_skip_counts)## [1] 2901 3text_2005_skip %>%
separate(skipgram, c("word1", "word2"), sep = " ") %>%
filter(!grepl(pattern = '[0-9]', x = word1)) %>%
filter(!grepl(pattern = '[0-9]', x = word2)) %>%
filter(!word1 %in% stop_words_es$word) %>%
filter(!word2 %in% stop_words_es$word) %>%
mutate(word1 = chartr(old = names(replacement_list) %>% str_c(collapse = ''),
new = replacement_list %>% str_c(collapse = ''),
x = word1)) %>%
mutate(word2 = chartr(old = names(replacement_list) %>% str_c(collapse = ''),
new = replacement_list %>% str_c(collapse = ''),
x = word2)) %>%
filter(!is.na(word1)) %>%
filter(!is.na(word2)) %>%
count(word1, word2, sort = TRUE) %>%
rename(weight = n) -> text_2005_skip_counts
dim(text_2005_skip_counts)## [1] 1580 3text_2008_skip %>%
separate(skipgram, c("word1", "word2"), sep = " ") %>%
filter(!grepl(pattern = '[0-9]', x = word1)) %>%
filter(!grepl(pattern = '[0-9]', x = word2)) %>%
filter(!word1 %in% stop_words_es$word) %>%
filter(!word2 %in% stop_words_es$word) %>%
mutate(word1 = chartr(old = names(replacement_list) %>% str_c(collapse = ''),
new = replacement_list %>% str_c(collapse = ''),
x = word1)) %>%
mutate(word2 = chartr(old = names(replacement_list) %>% str_c(collapse = ''),
new = replacement_list %>% str_c(collapse = ''),
x = word2)) %>%
filter(!is.na(word1)) %>%
filter(!is.na(word2)) %>%
count(word1, word2, sort = TRUE) %>%
rename(weight = n) -> text_2008_skip_counts
dim(text_2008_skip_counts)## [1] 3065 3text_2010_skip %>%
separate(skipgram, c("word1", "word2"), sep = " ") %>%
filter(!grepl(pattern = '[0-9]', x = word1)) %>%
filter(!grepl(pattern = '[0-9]', x = word2)) %>%
filter(!word1 %in% stop_words_es$word) %>%
filter(!word2 %in% stop_words_es$word) %>%
mutate(word1 = chartr(old = names(replacement_list) %>% str_c(collapse = ''),
new = replacement_list %>% str_c(collapse = ''),
x = word1)) %>%
mutate(word2 = chartr(old = names(replacement_list) %>% str_c(collapse = ''),
new = replacement_list %>% str_c(collapse = ''),
x = word2)) %>%
filter(!is.na(word1)) %>%
filter(!is.na(word2)) %>%
count(word1, word2, sort = TRUE) %>%
rename(weight = n) -> text_2010_skip_counts
dim(text_2010_skip_counts)## [1] 1219 3head(text_1997_skip_counts, n = 10)## # A tibble: 10 × 3
## word1 word2 weight
## <chr> <chr> <int>
## 1 rap city 8
## 2 correo electronico 7
## 3 punto vista 7
## 4 street journal 6
## 5 wall journal 6
## 6 wall street 6
## 7 apple deberia 5
## 8 creadores clones 5
## 9 hardware apple 5
## 10 productos realmente 5head(text_2001_skip_counts, n = 10)## # A tibble: 10 × 3
## word1 word2 weight
## <chr> <chr> <int>
## 1 mac os 52
## 2 sistema operativo 9
## 3 super drive 9
## 4 power mac 8
## 5 gracias steve 7
## 6 disco duro 6
## 7 disponible mac 6
## 8 etapas pipeline 6
## 9 power max 6
## 10 centro comercial 5head(text_2005_skip_counts, n = 10)## # A tibble: 10 × 3
## word1 word2 weight
## <chr> <chr> <int>
## 1 sistema operativo 12
## 2 mac os 10
## 3 binarios universales 7
## 4 has visto 7
## 5 codigo fuente 6
## 6 procesadores intel 6
## 7 año viene 5
## 8 dejame abrir 5
## 9 excelentes productos 5
## 10 powerpc intel 5head(text_2008_skip_counts, n = 10)## # A tibble: 10 × 3
## word1 word2 weight
## <chr> <chr> <int>
## 1 correo electronico 25
## 2 gustaria invitar 12
## 3 software iphone 12
## 4 app store 11
## 5 mis contactos 8
## 6 barra herramientas 6
## 7 directamente tu 6
## 8 interfaz usuario 6
## 9 sdk iphone 6
## 10 sitio web 6head(text_2010_skip_counts, n = 10)## # A tibble: 10 × 3
## word1 word2 weight
## <chr> <chr> <int>
## 1 pantalla retina 14
## 2 acero inoxidable 7
## 3 wi fi 7
## 4 estaciones base 5
## 5 pixeles pulgada 5
## 6 tu telefono 5
## 7 alta definicion 4
## 8 directamente tu 4
## 9 flash led 4
## 10 imovie iphone 4##### definir una red a partir de la frecuencia (weight) de los bigramas
g <- text_1997_skip_counts %>%
filter(weight > 1) %>%
graph_from_data_frame(directed = FALSE)
g <- igraph::simplify(g) # importante!
# grafo inducido por la componente conexa
V(g)$cluster <- clusters(graph = g)$membership
gcc <- induced_subgraph(graph = g, vids = which(V(g)$cluster == which.max(clusters(graph = g)$csize)))
par(mfrow = c(1,2), mar = c(1,1,2,1), mgp = c(1,1,1))
# viz 1
set.seed(123)
plot(gcc, layout = layout_with_fr, vertex.color = 1, vertex.frame.color = 1, vertex.size = 3, vertex.label = NA)
# viz 2
set.seed(123)
plot(gcc, layout = layout_with_fr, vertex.color = adjustcolor('darkolivegreen4', 0.1), vertex.frame.color = 'darkolivegreen4', vertex.size = 2*strength(gcc), vertex.label = NA)
title(main = "Componente conexa", outer = T, line = -1)
##### definir una red a partir de la frecuencia (weight) de los bigramas
g <- text_2001_skip_counts %>%
filter(weight > 1) %>%
graph_from_data_frame(directed = FALSE)
g <- igraph::simplify(g) # importante!
# grafo inducido por la componente conexa
V(g)$cluster <- clusters(graph = g)$membership
gcc <- induced_subgraph(graph = g, vids = which(V(g)$cluster == which.max(clusters(graph = g)$csize)))
par(mfrow = c(1,2), mar = c(1,1,2,1), mgp = c(1,1,1))
# viz 1
set.seed(123)
plot(gcc, layout = layout_with_fr, vertex.color = 1, vertex.frame.color = 1, vertex.size = 3, vertex.label = NA)
# viz 2
set.seed(123)
plot(gcc, layout = layout_with_fr, vertex.color = adjustcolor('darkolivegreen4', 0.1), vertex.frame.color = 'darkolivegreen4', vertex.size = 2*strength(gcc), vertex.label = NA)
title(main = "Componente conexa", outer = T, line = -1)
##### definir una red a partir de la frecuencia (weight) de los bigramas
g <- text_2005_skip_counts %>%
filter(weight > 1) %>%
graph_from_data_frame(directed = FALSE)
g <- igraph::simplify(g) # importante!
# grafo inducido por la componente conexa
V(g)$cluster <- clusters(graph = g)$membership
gcc <- induced_subgraph(graph = g, vids = which(V(g)$cluster == which.max(clusters(graph = g)$csize)))
par(mfrow = c(1,2), mar = c(1,1,2,1), mgp = c(1,1,1))
# viz 1
set.seed(123)
plot(gcc, layout = layout_with_fr, vertex.color = 1, vertex.frame.color = 1, vertex.size = 3, vertex.label = NA)
# viz 2
set.seed(123)
plot(gcc, layout = layout_with_fr, vertex.color = adjustcolor('darkolivegreen4', 0.1), vertex.frame.color = 'darkolivegreen4', vertex.size = 2*strength(gcc), vertex.label = NA)
title(main = "Componente conexa", outer = T, line = -1)
##### definir una red a partir de la frecuencia (weight) de los bigramas
g <- text_2008_skip_counts %>%
filter(weight > 1) %>%
graph_from_data_frame(directed = FALSE)
g <- igraph::simplify(g) # importante!
# grafo inducido por la componente conexa
V(g)$cluster <- clusters(graph = g)$membership
gcc <- induced_subgraph(graph = g, vids = which(V(g)$cluster == which.max(clusters(graph = g)$csize)))
par(mfrow = c(1,2), mar = c(1,1,2,1), mgp = c(1,1,1))
# viz 1
set.seed(123)
plot(gcc, layout = layout_with_fr, vertex.color = 1, vertex.frame.color = 1, vertex.size = 3, vertex.label = NA)
# viz 2
set.seed(123)
plot(gcc, layout = layout_with_fr, vertex.color = adjustcolor('darkolivegreen4', 0.1), vertex.frame.color = 'darkolivegreen4', vertex.size = 2*strength(gcc), vertex.label = NA)
title(main = "Componente conexa", outer = T, line = -1)
##### definir una red a partir de la frecuencia (weight) de los bigramas
g <- text_2010_skip_counts %>%
filter(weight > 1) %>%
graph_from_data_frame(directed = FALSE)
g <- igraph::simplify(g) # importante!
# grafo inducido por la componente conexa
V(g)$cluster <- clusters(graph = g)$membership
gcc <- induced_subgraph(graph = g, vids = which(V(g)$cluster == which.max(clusters(graph = g)$csize)))
par(mfrow = c(1,2), mar = c(1,1,2,1), mgp = c(1,1,1))
# viz 1
set.seed(123)
plot(gcc, layout = layout_with_fr, vertex.color = 1, vertex.frame.color = 1, vertex.size = 3, vertex.label = NA)
# viz 2
set.seed(123)
plot(gcc, layout = layout_with_fr, vertex.color = adjustcolor('darkolivegreen4', 0.1), vertex.frame.color = 'darkolivegreen4', vertex.size = 2*strength(gcc), vertex.label = NA)
title(main = "Componente conexa", outer = T, line = -1)